Overview

Dataset statistics

Number of variables27
Number of observations4203040
Missing cells4467105
Missing cells (%)3.9%
Duplicate rows6866
Duplicate rows (%)0.2%
Total size in memory897.9 MiB
Average record size in memory224.0 B

Variable types

Categorical8
DateTime2
Numeric16
Unsupported1

Alerts

Dataset has 6866 (0.2%) duplicate rowsDuplicates
VIN has a high cardinality: 3541778 distinct valuesHigh cardinality
TypMot has a high cardinality: 59720 distinct valuesHigh cardinality
TZn has a high cardinality: 6385 distinct valuesHigh cardinality
ObchOznTyp has a high cardinality: 78899 distinct valuesHigh cardinality
Ct has a high cardinality: 150 distinct valuesHigh cardinality
DrTP is highly imbalanced (63.6%)Imbalance
TZn is highly imbalanced (59.4%)Imbalance
DrVoz is highly imbalanced (73.0%)Imbalance
Ct is highly imbalanced (74.0%)Imbalance
VyslSTK is highly imbalanced (77.7%)Imbalance
TypMot has 207440 (4.9%) missing valuesMissing
VyslEmise has 4203040 (100.0%) missing valuesMissing
ZavC is highly skewed (γ1 = 20.82974482)Skewed
Zav9 is highly skewed (γ1 = 57.53775705)Skewed
VIN is uniformly distributedUniform
VyslEmise is an unsupported type, check if it needs cleaning or further analysisUnsupported
Km has 320601 (7.6%) zerosZeros
ZavA has 2106231 (50.1%) zerosZeros
ZavB has 3961301 (94.2%) zerosZeros
ZavC has 4178080 (99.4%) zerosZeros
Zav0 has 3955321 (94.1%) zerosZeros
Zav1 has 3059185 (72.8%) zerosZeros
Zav2 has 3878715 (92.3%) zerosZeros
Zav3 has 3887919 (92.5%) zerosZeros
Zav4 has 3283109 (78.1%) zerosZeros
Zav5 has 3574221 (85.0%) zerosZeros
Zav6 has 2440814 (58.1%) zerosZeros
Zav7 has 4175613 (99.3%) zerosZeros
Zav8 has 4165235 (99.1%) zerosZeros
Zav9 has 4200626 (99.9%) zerosZeros

Reproduction

Analysis started2023-04-01 12:50:37.039290
Analysis finished2023-04-01 12:53:22.710284
Duration2 minutes and 45.67 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

DrTP
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.1 MiB
pravidelná
2823546 
Evidenční kontrola
947202 
Před registrací
 
193427
opakovaná
 
180208
Technická silniční kontrola
 
21392
Other values (9)
 
37265

Length

Max length46
Median length10
Mean length12.171027
Min length3

Characters and Unicode

Total characters51155314
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpravidelná
2nd rowpravidelná
3rd rowEvidenční kontrola
4th rowpravidelná
5th rowpravidelná

Common Values

ValueCountFrequency (%)
pravidelná 2823546
67.2%
Evidenční kontrola 947202
 
22.5%
Před registrací 193427
 
4.6%
opakovaná 180208
 
4.3%
Technická silniční kontrola 21392
 
0.5%
Na žádost zákazníka 20446
 
0.5%
Před schvál. tech. způsob. vozidla 6412
 
0.2%
ADR 4530
 
0.1%
Před registrací - opakovaná 4235
 
0.1%
TSK - Opakovaná po DN 584
 
< 0.1%
Other values (4) 1058
 
< 0.1%

Length

2023-04-01T14:53:22.745309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pravidelná 2823546
51.6%
kontrola 968594
 
17.7%
evidenční 947202
 
17.3%
před 204273
 
3.7%
registrací 197662
 
3.6%
opakovaná 186036
 
3.4%
technická 21441
 
0.4%
silniční 21392
 
0.4%
na 20446
 
0.4%
žádost 20446
 
0.4%
Other values (12) 59908
 
1.1%

Most occurring characters

ValueCountFrequency (%)
n 5957300
11.6%
a 4429921
8.7%
e 4200784
8.2%
r 4187513
8.2%
i 4039246
 
7.9%
d 4002127
 
7.8%
v 3970006
 
7.8%
l 3826803
 
7.5%
á 3078575
 
6.0%
p 3016826
 
5.9%
Other values (27) 10446213
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48648321
95.1%
Space Separator 1267906
 
2.5%
Uppercase Letter 1213426
 
2.4%
Other Punctuation 19833
 
< 0.1%
Dash Punctuation 5828
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5957300
12.2%
a 4429921
9.1%
e 4200784
8.6%
r 4187513
8.6%
i 4039246
8.3%
d 4002127
8.2%
v 3970006
8.2%
l 3826803
7.9%
á 3078575
 
6.3%
p 3016826
 
6.2%
Other values (14) 7939220
16.3%
Uppercase Letter
ValueCountFrequency (%)
E 947202
78.1%
P 204273
 
16.8%
T 22516
 
1.9%
N 21079
 
1.7%
D 5384
 
0.4%
A 4800
 
0.4%
R 4800
 
0.4%
S 1124
 
0.1%
K 1124
 
0.1%
O 1124
 
0.1%
Space Separator
ValueCountFrequency (%)
1267906
100.0%
Other Punctuation
ValueCountFrequency (%)
. 19833
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5828
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49861747
97.5%
Common 1293567
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5957300
11.9%
a 4429921
8.9%
e 4200784
8.4%
r 4187513
8.4%
i 4039246
8.1%
d 4002127
8.0%
v 3970006
8.0%
l 3826803
7.7%
á 3078575
 
6.2%
p 3016826
 
6.1%
Other values (24) 9152646
18.4%
Common
ValueCountFrequency (%)
1267906
98.0%
. 19833
 
1.5%
- 5828
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45689966
89.3%
None 5465348
 
10.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5957300
13.0%
a 4429921
9.7%
e 4200784
9.2%
r 4187513
9.2%
i 4039246
8.8%
d 4002127
8.8%
v 3970006
8.7%
l 3826803
8.4%
p 3016826
6.6%
o 2342437
 
5.1%
Other values (21) 5717003
12.5%
None
ValueCountFrequency (%)
á 3078575
56.3%
í 1186800
 
21.7%
č 968594
 
17.7%
ř 204322
 
3.7%
ž 20446
 
0.4%
ů 6611
 
0.1%

VIN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3541778
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Memory size64.1 MiB
001
 
34
012
 
23
123
 
21
013
 
21
003
 
20
Other values (3541773)
4202921 

Length

Max length21
Median length17
Mean length16.555904
Min length1

Characters and Unicode

Total characters69585126
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2987793 ?
Unique (%)71.1%

Sample

1st row1/12-1802
2nd rowTM1V023106S000023
3rd rowWAFZLAF103K026713
4th rowWAFZKAF18DK036669
5th rowWAFZPAF11EK037060

Common Values

ValueCountFrequency (%)
001 34
 
< 0.1%
012 23
 
< 0.1%
123 21
 
< 0.1%
013 21
 
< 0.1%
003 20
 
< 0.1%
002 20
 
< 0.1%
TEST0000000000001 20
 
< 0.1%
011 19
 
< 0.1%
005 19
 
< 0.1%
010 19
 
< 0.1%
Other values (3541768) 4202824
> 99.9%

Length

2023-04-01T14:53:22.923827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
001 34
 
< 0.1%
012 23
 
< 0.1%
123 21
 
< 0.1%
013 21
 
< 0.1%
003 20
 
< 0.1%
002 20
 
< 0.1%
test0000000000001 20
 
< 0.1%
011 19
 
< 0.1%
005 19
 
< 0.1%
010 19
 
< 0.1%
Other values (3541727) 4202864
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 7018469
 
10.1%
1 5482737
 
7.9%
2 4279684
 
6.2%
3 3994484
 
5.7%
5 3640958
 
5.2%
6 3549897
 
5.1%
4 3475054
 
5.0%
7 3293138
 
4.7%
Z 3020790
 
4.3%
8 2987974
 
4.3%
Other values (34) 28841941
41.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40496095
58.2%
Uppercase Letter 29021697
41.7%
Dash Punctuation 36946
 
0.1%
Other Punctuation 30346
 
< 0.1%
Space Separator 40
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 3020790
 
10.4%
B 2265876
 
7.8%
W 1994919
 
6.9%
M 1853833
 
6.4%
F 1831165
 
6.3%
A 1787662
 
6.2%
T 1773584
 
6.1%
V 1406541
 
4.8%
J 1365357
 
4.7%
C 1100986
 
3.8%
Other values (16) 10620984
36.6%
Decimal Number
ValueCountFrequency (%)
0 7018469
17.3%
1 5482737
13.5%
2 4279684
10.6%
3 3994484
9.9%
5 3640958
9.0%
6 3549897
8.8%
4 3475054
8.6%
7 3293138
8.1%
8 2987974
7.4%
9 2773700
 
6.8%
Other Punctuation
ValueCountFrequency (%)
/ 29286
96.5%
. 707
 
2.3%
% 271
 
0.9%
* 76
 
0.3%
, 6
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 36946
100.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40563429
58.3%
Latin 29021697
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 3020790
 
10.4%
B 2265876
 
7.8%
W 1994919
 
6.9%
M 1853833
 
6.4%
F 1831165
 
6.3%
A 1787662
 
6.2%
T 1773584
 
6.1%
V 1406541
 
4.8%
J 1365357
 
4.7%
C 1100986
 
3.8%
Other values (16) 10620984
36.6%
Common
ValueCountFrequency (%)
0 7018469
17.3%
1 5482737
13.5%
2 4279684
10.6%
3 3994484
9.8%
5 3640958
9.0%
6 3549897
8.8%
4 3475054
8.6%
7 3293138
8.1%
8 2987974
7.4%
9 2773700
 
6.8%
Other values (8) 67334
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69585126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7018469
 
10.1%
1 5482737
 
7.9%
2 4279684
 
6.2%
3 3994484
 
5.7%
5 3640958
 
5.2%
6 3549897
 
5.1%
4 3475054
 
5.0%
7 3293138
 
4.7%
Z 3020790
 
4.3%
8 2987974
 
4.3%
Other values (34) 28841941
41.4%
Distinct362
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.1 MiB
Minimum2021-01-02 00:00:00
Maximum2021-12-31 00:00:00
2023-04-01T14:53:23.018225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:53:23.103990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TypMot
Categorical

HIGH CARDINALITY  MISSING 

Distinct59720
Distinct (%)1.5%
Missing207440
Missing (%)4.9%
Memory size64.1 MiB
-
 
103064
BXE
 
35902
ALH
 
34451
CJZ
 
33982
G4FA
 
29290
Other values (59715)
3758911 

Length

Max length17
Median length16
Mean length4.4727923
Min length1

Characters and Unicode

Total characters17871489
Distinct characters107
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29238 ?
Unique (%)0.7%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 103064
 
2.5%
BXE 35902
 
0.9%
ALH 34451
 
0.8%
CJZ 33982
 
0.8%
G4FA 29290
 
0.7%
BME 24617
 
0.6%
BLS 24540
 
0.6%
ASV 24334
 
0.6%
CBZA 23257
 
0.6%
BSE 23021
 
0.5%
Other values (59710) 3639142
86.6%
(Missing) 207440
 
4.9%

Length

2023-04-01T14:53:23.195100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
108083
 
2.4%
bxe 35903
 
0.8%
alh 34493
 
0.8%
cjz 33983
 
0.8%
7 31669
 
0.7%
g4fa 29292
 
0.7%
bme 24630
 
0.6%
bls 24575
 
0.6%
asv 24374
 
0.5%
m 23924
 
0.5%
Other values (44491) 4088248
91.7%

Most occurring characters

ValueCountFrequency (%)
A 1334911
 
7.5%
1 1052420
 
5.9%
B 959176
 
5.4%
C 908487
 
5.1%
F 893409
 
5.0%
4 887783
 
5.0%
D 885019
 
5.0%
0 816539
 
4.6%
2 612869
 
3.4%
6 558421
 
3.1%
Other values (97) 8962455
50.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10849719
60.7%
Decimal Number 6047113
33.8%
Space Separator 465123
 
2.6%
Other Punctuation 286207
 
1.6%
Dash Punctuation 215756
 
1.2%
Math Symbol 1962
 
< 0.1%
Open Punctuation 1887
 
< 0.1%
Close Punctuation 1864
 
< 0.1%
Lowercase Letter 1853
 
< 0.1%
Other Symbol 2
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1334911
 
12.3%
B 959176
 
8.8%
C 908487
 
8.4%
F 893409
 
8.2%
D 885019
 
8.2%
H 536572
 
4.9%
E 503610
 
4.6%
M 486122
 
4.5%
Z 405233
 
3.7%
G 379168
 
3.5%
Other values (32) 3558012
32.8%
Lowercase Letter
ValueCountFrequency (%)
a 192
 
10.4%
c 145
 
7.8%
b 141
 
7.6%
s 134
 
7.2%
e 107
 
5.8%
d 106
 
5.7%
f 105
 
5.7%
l 97
 
5.2%
h 75
 
4.0%
m 74
 
4.0%
Other values (23) 677
36.5%
Other Punctuation
ValueCountFrequency (%)
. 237185
82.9%
/ 32473
 
11.3%
, 9067
 
3.2%
* 7041
 
2.5%
? 360
 
0.1%
; 45
 
< 0.1%
: 25
 
< 0.1%
' 5
 
< 0.1%
" 3
 
< 0.1%
@ 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1052420
17.4%
4 887783
14.7%
0 816539
13.5%
2 612869
10.1%
6 558421
9.2%
3 480433
7.9%
7 442593
7.3%
8 413568
 
6.8%
9 407830
 
6.7%
5 374657
 
6.2%
Math Symbol
ValueCountFrequency (%)
+ 1960
99.9%
| 2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1886
99.9%
{ 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 1863
99.9%
} 1
 
0.1%
Space Separator
ValueCountFrequency (%)
465123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 215756
100.0%
Other Symbol
ValueCountFrequency (%)
° 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10851572
60.7%
Common 7019917
39.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1334911
 
12.3%
B 959176
 
8.8%
C 908487
 
8.4%
F 893409
 
8.2%
D 885019
 
8.2%
H 536572
 
4.9%
E 503610
 
4.6%
M 486122
 
4.5%
Z 405233
 
3.7%
G 379168
 
3.5%
Other values (65) 3559865
32.8%
Common
ValueCountFrequency (%)
1 1052420
15.0%
4 887783
12.6%
0 816539
11.6%
2 612869
8.7%
6 558421
8.0%
3 480433
6.8%
465123
6.6%
7 442593
6.3%
8 413568
 
5.9%
9 407830
 
5.8%
Other values (22) 882338
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17866678
> 99.9%
None 4811
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1334911
 
7.5%
1 1052420
 
5.9%
B 959176
 
5.4%
C 908487
 
5.1%
F 893409
 
5.0%
4 887783
 
5.0%
D 885019
 
5.0%
0 816539
 
4.6%
2 612869
 
3.4%
6 558421
 
3.1%
Other values (72) 8957644
50.1%
None
ValueCountFrequency (%)
Š 3507
72.9%
Č 787
 
16.4%
Á 152
 
3.2%
Ý 109
 
2.3%
Ř 78
 
1.6%
Í 65
 
1.4%
Ž 30
 
0.6%
š 28
 
0.6%
ý 12
 
0.2%
Ě 10
 
0.2%
Other values (15) 33
 
0.7%

TZn
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct6385
Distinct (%)0.2%
Missing88
Missing (%)< 0.1%
Memory size64.1 MiB
ŠKODA
1050457 
FORD
274834 
VOLKSWAGEN
257022 
PEUGEOT
 
183197
RENAULT
 
181163
Other values (6380)
2256279 

Length

Max length30
Median length29
Mean length5.8510064
Min length1

Characters and Unicode

Total characters24591499
Distinct characters112
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3022 ?
Unique (%)0.1%

Sample

1st rowACK
2nd rowACK
3rd rowACKERMANN
4th rowACKERMANN
5th rowACKERMANN

Common Values

ValueCountFrequency (%)
ŠKODA 1050457
25.0%
FORD 274834
 
6.5%
VOLKSWAGEN 257022
 
6.1%
PEUGEOT 183197
 
4.4%
RENAULT 181163
 
4.3%
VW 165106
 
3.9%
CITROËN 135591
 
3.2%
MERCEDES-BENZ 130775
 
3.1%
HYUNDAI 121038
 
2.9%
OPEL 114996
 
2.7%
Other values (6375) 1588773
37.8%

Length

2023-04-01T14:53:23.286114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
škoda 1050511
24.3%
ford 274843
 
6.4%
volkswagen 257023
 
5.9%
peugeot 183198
 
4.2%
renault 181256
 
4.2%
vw 165106
 
3.8%
citroën 135591
 
3.1%
mercedes-benz 130797
 
3.0%
hyundai 121040
 
2.8%
opel 114996
 
2.7%
Other values (6077) 1705879
39.5%

Most occurring characters

ValueCountFrequency (%)
A 3000273
 
12.2%
O 2742964
 
11.2%
D 1954697
 
7.9%
E 1838208
 
7.5%
K 1517610
 
6.2%
N 1187258
 
4.8%
R 1090520
 
4.4%
T 1068688
 
4.3%
Š 1053556
 
4.3%
I 952354
 
3.9%
Other values (102) 8185371
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24305180
98.8%
Dash Punctuation 140846
 
0.6%
Space Separator 131025
 
0.5%
Other Punctuation 6705
 
< 0.1%
Decimal Number 5413
 
< 0.1%
Lowercase Letter 2078
 
< 0.1%
Math Symbol 118
 
< 0.1%
Close Punctuation 64
 
< 0.1%
Open Punctuation 63
 
< 0.1%
Modifier Symbol 7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3000273
 
12.3%
O 2742964
 
11.3%
D 1954697
 
8.0%
E 1838208
 
7.6%
K 1517610
 
6.2%
N 1187258
 
4.9%
R 1090520
 
4.5%
T 1068688
 
4.4%
Š 1053556
 
4.3%
I 952354
 
3.9%
Other values (38) 7899052
32.5%
Lowercase Letter
ValueCountFrequency (%)
a 234
 
11.3%
e 214
 
10.3%
n 183
 
8.8%
o 172
 
8.3%
r 164
 
7.9%
l 114
 
5.5%
c 106
 
5.1%
i 90
 
4.3%
s 84
 
4.0%
t 82
 
3.9%
Other values (30) 635
30.6%
Decimal Number
ValueCountFrequency (%)
0 1138
21.0%
5 1133
20.9%
1 1124
20.8%
2 648
12.0%
3 474
8.8%
6 400
 
7.4%
7 200
 
3.7%
8 133
 
2.5%
4 125
 
2.3%
9 38
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 5890
87.8%
/ 331
 
4.9%
& 272
 
4.1%
, 207
 
3.1%
* 4
 
0.1%
: 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 140822
> 99.9%
24
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 6
85.7%
¨ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
131025
100.0%
Math Symbol
ValueCountFrequency (%)
+ 118
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24307258
98.8%
Common 284241
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3000273
 
12.3%
O 2742964
 
11.3%
D 1954697
 
8.0%
E 1838208
 
7.6%
K 1517610
 
6.2%
N 1187258
 
4.9%
R 1090520
 
4.5%
T 1068688
 
4.4%
Š 1053556
 
4.3%
I 952354
 
3.9%
Other values (78) 7901130
32.5%
Common
ValueCountFrequency (%)
- 140822
49.5%
131025
46.1%
. 5890
 
2.1%
0 1138
 
0.4%
5 1133
 
0.4%
1 1124
 
0.4%
2 648
 
0.2%
3 474
 
0.2%
6 400
 
0.1%
/ 331
 
0.1%
Other values (14) 1256
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23306476
94.8%
None 1284999
 
5.2%
Punctuation 24
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3000273
12.9%
O 2742964
 
11.8%
D 1954697
 
8.4%
E 1838208
 
7.9%
K 1517610
 
6.5%
N 1187258
 
5.1%
R 1090520
 
4.7%
T 1068688
 
4.6%
I 952354
 
4.1%
S 936089
 
4.0%
Other values (63) 7017815
30.1%
None
ValueCountFrequency (%)
Š 1053556
82.0%
Ë 135591
 
10.6%
Í 30132
 
2.3%
Ý 29766
 
2.3%
Ü 12886
 
1.0%
Á 6439
 
0.5%
Ö 5983
 
0.5%
Č 5018
 
0.4%
Ě 1434
 
0.1%
Ř 1297
 
0.1%
Other values (28) 2897
 
0.2%
Punctuation
ValueCountFrequency (%)
24
100.0%

DrVoz
Categorical

Distinct47
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size64.1 MiB
OSOBNÍ AUTOMOBIL
3136592 
NÁKLADNÍ AUTOMOBIL
484187 
MOTOCYKL
 
168639
NÁKLADNÍ PŘÍVĚS
 
162269
PŘÍPOJNÉ VOZIDLO
 
49099
Other values (42)
 
202252

Length

Max length30
Median length16
Mean length15.835247
Min length5

Characters and Unicode

Total characters66556145
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNÁKLADNÍ PŘÍVĚS
2nd rowNÁKLADNÍ PŘÍVĚS
3rd rowNÁKLADNÍ PŘÍVĚS
4th rowPŘÍPOJNÉ VOZIDLO
5th rowNÁKLADNÍ PŘÍVĚS

Common Values

ValueCountFrequency (%)
OSOBNÍ AUTOMOBIL 3136592
74.6%
NÁKLADNÍ AUTOMOBIL 484187
 
11.5%
MOTOCYKL 168639
 
4.0%
NÁKLADNÍ PŘÍVĚS 162269
 
3.9%
PŘÍPOJNÉ VOZIDLO 49099
 
1.2%
NÁKLADNÍ NÁVĚS 45508
 
1.1%
TRAKTOR KOLOVÝ 25286
 
0.6%
VOZIDLO ZVLÁŠTNÍHO URČENÍ 23388
 
0.6%
AUTOBUS 19517
 
0.5%
SPECIÁLNÍ AUTOMOBIL 17676
 
0.4%
Other values (37) 70877
 
1.7%

Length

2023-04-01T14:53:23.373040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
automobil 3638456
44.2%
osobní 3136592
38.1%
nákladní 698576
 
8.5%
přívěs 186419
 
2.3%
motocykl 168639
 
2.0%
vozidlo 78432
 
1.0%
návěs 52529
 
0.6%
přípojné 49099
 
0.6%
traktor 39356
 
0.5%
speciální 26849
 
0.3%
Other values (44) 164206
 
2.0%

Most occurring characters

ValueCountFrequency (%)
O 14288926
21.5%
B 6795222
10.2%
N 4734378
 
7.1%
L 4661564
 
7.0%
A 4456406
 
6.7%
Í 4156034
 
6.2%
4036225
 
6.1%
T 3988307
 
6.0%
M 3812730
 
5.7%
I 3743797
 
5.6%
Other values (24) 11882556
17.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 62516284
93.9%
Space Separator 4036225
 
6.1%
Other Punctuation 3635
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 14288926
22.9%
B 6795222
10.9%
N 4734378
 
7.6%
L 4661564
 
7.5%
A 4456406
 
7.1%
Í 4156034
 
6.6%
T 3988307
 
6.4%
M 3812730
 
6.1%
I 3743797
 
6.0%
U 3705374
 
5.9%
Other values (21) 8173546
13.1%
Space Separator
ValueCountFrequency (%)
4036225
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3635
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62516284
93.9%
Common 4039861
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 14288926
22.9%
B 6795222
10.9%
N 4734378
 
7.6%
L 4661564
 
7.5%
A 4456406
 
7.1%
Í 4156034
 
6.6%
T 3988307
 
6.4%
M 3812730
 
6.1%
I 3743797
 
6.0%
U 3705374
 
5.9%
Other values (21) 8173546
13.1%
Common
ValueCountFrequency (%)
4036225
99.9%
. 3635
 
0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60934994
91.6%
None 5621151
 
8.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 14288926
23.4%
B 6795222
11.2%
N 4734378
 
7.8%
L 4661564
 
7.7%
A 4456406
 
7.3%
4036225
 
6.6%
T 3988307
 
6.5%
M 3812730
 
6.3%
I 3743797
 
6.1%
U 3705374
 
6.1%
Other values (14) 6712065
11.0%
None
ValueCountFrequency (%)
Í 4156034
73.9%
Á 810057
 
14.4%
Ě 247842
 
4.4%
Ř 237267
 
4.2%
É 54292
 
1.0%
Ý 50314
 
0.9%
Č 33456
 
0.6%
Š 23388
 
0.4%
Ů 8378
 
0.1%
Ž 123
 
< 0.1%

ObchOznTyp
Categorical

Distinct78899
Distinct (%)1.9%
Missing162
Missing (%)< 0.1%
Memory size64.1 MiB
OCTAVIA
 
132816
FABIA
 
112416
OCTAVIA (1Z)
 
103098
FABIA (5J)
 
93156
OCTAVIA (5E)
 
75941
Other values (78894)
3685451 

Length

Max length40
Median length34
Mean length8.2612419
Min length1

Characters and Unicode

Total characters34720992
Distinct characters114
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36545 ?
Unique (%)0.9%

Sample

1st row2500
2nd row2000 A
3rd rowz-la-f 10,5/7,4e
4th rowZ-KA-F18/7
5th rowZ-PA-F10.5

Common Values

ValueCountFrequency (%)
OCTAVIA 132816
 
3.2%
FABIA 112416
 
2.7%
OCTAVIA (1Z) 103098
 
2.5%
FABIA (5J) 93156
 
2.2%
OCTAVIA (5E) 75941
 
1.8%
FABIA (6Y) 74138
 
1.8%
OCTAVIA (1U) 57322
 
1.4%
FELICIA 52635
 
1.3%
GOLF 46433
 
1.1%
SUPERB (3T) 43612
 
1.0%
Other values (78889) 3411311
81.2%

Length

2023-04-01T14:53:23.463776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
octavia 413393
 
6.0%
fabia 343810
 
5.0%
5j 119203
 
1.7%
1z 117110
 
1.7%
6y 114788
 
1.7%
combi 114488
 
1.7%
golf 105957
 
1.5%
passat 93126
 
1.4%
5e 76929
 
1.1%
focus 75963
 
1.1%
Other values (41034) 5284454
77.0%

Most occurring characters

ValueCountFrequency (%)
A 3752865
 
10.8%
2793049
 
8.0%
I 1898915
 
5.5%
O 1803503
 
5.2%
T 1661392
 
4.8%
( 1543919
 
4.4%
) 1542647
 
4.4%
C 1519077
 
4.4%
R 1426284
 
4.1%
S 1349112
 
3.9%
Other values (104) 15430229
44.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 23886350
68.8%
Decimal Number 4519099
 
13.0%
Space Separator 2793049
 
8.0%
Open Punctuation 1543919
 
4.4%
Close Punctuation 1542647
 
4.4%
Dash Punctuation 172573
 
0.5%
Other Punctuation 122957
 
0.4%
Lowercase Letter 113957
 
0.3%
Modifier Symbol 24749
 
0.1%
Math Symbol 1684
 
< 0.1%
Other values (2) 8
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3752865
15.7%
I 1898915
 
7.9%
O 1803503
 
7.6%
T 1661392
 
7.0%
C 1519077
 
6.4%
R 1426284
 
6.0%
S 1349112
 
5.6%
E 1309290
 
5.5%
N 982578
 
4.1%
F 876078
 
3.7%
Other values (35) 7307256
30.6%
Lowercase Letter
ValueCountFrequency (%)
i 58018
50.9%
a 6623
 
5.8%
r 5989
 
5.3%
e 5378
 
4.7%
x 5205
 
4.6%
o 3918
 
3.4%
d 3607
 
3.2%
n 2900
 
2.5%
m 2694
 
2.4%
v 2537
 
2.2%
Other values (26) 17088
 
15.0%
Other Punctuation
ValueCountFrequency (%)
. 65315
53.1%
/ 49013
39.9%
* 3601
 
2.9%
, 3273
 
2.7%
! 1578
 
1.3%
' 76
 
0.1%
& 40
 
< 0.1%
; 35
 
< 0.1%
" 15
 
< 0.1%
@ 5
 
< 0.1%
Other values (3) 6
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 886117
19.6%
1 709008
15.7%
5 596951
13.2%
3 563572
12.5%
2 516739
11.4%
6 401214
8.9%
4 303213
 
6.7%
7 230242
 
5.1%
8 204958
 
4.5%
9 107085
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 172569
> 99.9%
4
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 24745
> 99.9%
¨ 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2793049
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1543919
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1542647
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1684
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24000307
69.1%
Common 10720685
30.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3752865
15.6%
I 1898915
 
7.9%
O 1803503
 
7.5%
T 1661392
 
6.9%
C 1519077
 
6.3%
R 1426284
 
5.9%
S 1349112
 
5.6%
E 1309290
 
5.5%
N 982578
 
4.1%
F 876078
 
3.7%
Other values (71) 7421213
30.9%
Common
ValueCountFrequency (%)
2793049
26.1%
( 1543919
14.4%
) 1542647
14.4%
0 886117
 
8.3%
1 709008
 
6.6%
5 596951
 
5.6%
3 563572
 
5.3%
2 516739
 
4.8%
6 401214
 
3.7%
4 303213
 
2.8%
Other values (23) 864256
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34635234
99.8%
None 85754
 
0.2%
Punctuation 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3752865
 
10.8%
2793049
 
8.1%
I 1898915
 
5.5%
O 1803503
 
5.2%
T 1661392
 
4.8%
( 1543919
 
4.5%
) 1542647
 
4.5%
C 1519077
 
4.4%
R 1426284
 
4.1%
S 1349112
 
3.9%
Other values (71) 15344471
44.3%
None
ValueCountFrequency (%)
´ 24745
28.9%
Ý 21428
25.0%
Í 20342
23.7%
Á 13826
16.1%
É 2144
 
2.5%
á 1026
 
1.2%
Č 573
 
0.7%
Ü 391
 
0.5%
í 337
 
0.4%
Š 321
 
0.4%
Other values (22) 621
 
0.7%
Punctuation
ValueCountFrequency (%)
4
100.0%

Ct
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct150
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size64.1 MiB
M1
3052348 
N1
 
295157
O1
 
137690
N3
 
117335
M1G
 
95117
Other values (145)
505391 

Length

Max length7
Median length2
Mean length2.0754238
Min length1

Characters and Unicode

Total characters8723085
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowO2
2nd rowO2
3rd rowO3
4th rowO4
5th rowO4

Common Values

ValueCountFrequency (%)
M1 3052348
72.6%
N1 295157
 
7.0%
O1 137690
 
3.3%
N3 117335
 
2.8%
M1G 95117
 
2.3%
O4 84008
 
2.0%
L3e 66784
 
1.6%
LC 62764
 
1.5%
N2 56278
 
1.3%
O2 51333
 
1.2%
Other values (140) 184224
 
4.4%

Length

2023-04-01T14:53:23.551053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m1 3052348
72.6%
n1 295157
 
7.0%
o1 137690
 
3.3%
n3 117335
 
2.8%
m1g 95117
 
2.3%
o4 84008
 
2.0%
l3e 66784
 
1.6%
lc 62764
 
1.5%
n2 56278
 
1.3%
o2 51333
 
1.2%
Other values (137) 184224
 
4.4%

Most occurring characters

ValueCountFrequency (%)
1 3657421
41.9%
M 3168580
36.3%
N 520558
 
6.0%
O 291962
 
3.3%
3 256148
 
2.9%
L 170489
 
2.0%
G 145011
 
1.7%
2 118031
 
1.4%
4 97671
 
1.1%
e 92158
 
1.1%
Other values (25) 205056
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4467193
51.2%
Decimal Number 4134680
47.4%
Lowercase Letter 104445
 
1.2%
Dash Punctuation 16241
 
0.2%
Space Separator 378
 
< 0.1%
Other Punctuation 148
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 3168580
70.9%
N 520558
 
11.7%
O 291962
 
6.5%
L 170489
 
3.8%
G 145011
 
3.2%
C 62880
 
1.4%
T 53999
 
1.2%
A 24992
 
0.6%
S 10676
 
0.2%
R 6262
 
0.1%
Other values (7) 11784
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 3657421
88.5%
3 256148
 
6.2%
2 118031
 
2.9%
4 97671
 
2.4%
7 3799
 
0.1%
5 1160
 
< 0.1%
6 450
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 92158
88.2%
a 10556
 
10.1%
b 1691
 
1.6%
z 35
 
< 0.1%
s 3
 
< 0.1%
n 1
 
< 0.1%
p 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 141
95.3%
* 7
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 16241
100.0%
Space Separator
ValueCountFrequency (%)
378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4571638
52.4%
Common 4151447
47.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 3168580
69.3%
N 520558
 
11.4%
O 291962
 
6.4%
L 170489
 
3.7%
G 145011
 
3.2%
e 92158
 
2.0%
C 62880
 
1.4%
T 53999
 
1.2%
A 24992
 
0.5%
S 10676
 
0.2%
Other values (14) 30333
 
0.7%
Common
ValueCountFrequency (%)
1 3657421
88.1%
3 256148
 
6.2%
2 118031
 
2.8%
4 97671
 
2.4%
- 16241
 
0.4%
7 3799
 
0.1%
5 1160
 
< 0.1%
6 450
 
< 0.1%
378
 
< 0.1%
. 141
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8723085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3657421
41.9%
M 3168580
36.3%
N 520558
 
6.0%
O 291962
 
3.3%
3 256148
 
2.9%
L 170489
 
2.0%
G 145011
 
1.7%
2 118031
 
1.4%
4 97671
 
1.1%
e 92158
 
1.1%
Other values (25) 205056
 
2.4%
Distinct22338
Distinct (%)0.5%
Missing28153
Missing (%)0.7%
Memory size64.1 MiB
Minimum1753-02-19 00:00:00
Maximum2030-11-03 00:00:00
2023-04-01T14:53:23.633144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:53:23.719094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Km
Real number (ℝ)

Distinct528551
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164770.36
Minimum0
Maximum9999999
Zeros320601
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:23.808239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q164006
median149112
Q3231902
95-th percentile378982
Maximum9999999
Range9999999
Interquartile range (IQR)167896

Descriptive statistics

Standard deviation144562.99
Coefficient of variation (CV)0.87736036
Kurtosis89.910078
Mean164770.36
Median Absolute Deviation (MAD)83999
Skewness4.0799196
Sum6.9253643 × 1011
Variance2.0898457 × 1010
MonotonicityNot monotonic
2023-04-01T14:53:23.899009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 320601
 
7.6%
1 1875
 
< 0.1%
4 850
 
< 0.1%
5 826
 
< 0.1%
6 684
 
< 0.1%
7 681
 
< 0.1%
9 677
 
< 0.1%
8 674
 
< 0.1%
10 639
 
< 0.1%
11 614
 
< 0.1%
Other values (528541) 3874919
92.2%
ValueCountFrequency (%)
0 320601
7.6%
1 1875
 
< 0.1%
2 487
 
< 0.1%
3 542
 
< 0.1%
4 850
 
< 0.1%
5 826
 
< 0.1%
6 684
 
< 0.1%
7 681
 
< 0.1%
8 674
 
< 0.1%
9 677
 
< 0.1%
ValueCountFrequency (%)
9999999 2
< 0.1%
9781163 1
< 0.1%
9558553 1
< 0.1%
9509943 1
< 0.1%
8520665 1
< 0.1%
8031335 1
< 0.1%
7982271 1
< 0.1%
7567843 1
< 0.1%
7438898 1
< 0.1%
7399322 1
< 0.1%

VyslSTK
Categorical

Distinct3
Distinct (%)< 0.1%
Missing65
Missing (%)< 0.1%
Memory size64.1 MiB
způsobilé
3954046 
částečně způsobilé
 
221601
nezpůsobilé
 
27328

Length

Max length18
Median length9
Mean length9.4875273
Min length9

Characters and Unicode

Total characters39875840
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowzpůsobilé
2nd rowzpůsobilé
3rd rowzpůsobilé
4th rowzpůsobilé
5th rowzpůsobilé

Common Values

ValueCountFrequency (%)
způsobilé 3954046
94.1%
částečně způsobilé 221601
 
5.3%
nezpůsobilé 27328
 
0.7%
(Missing) 65
 
< 0.1%

Length

2023-04-01T14:53:23.976750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T14:53:24.052033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
způsobilé 4175647
94.4%
částečně 221601
 
5.0%
nezpůsobilé 27328
 
0.6%

Most occurring characters

ValueCountFrequency (%)
s 4424576
11.1%
z 4202975
10.5%
p 4202975
10.5%
ů 4202975
10.5%
o 4202975
10.5%
b 4202975
10.5%
i 4202975
10.5%
l 4202975
10.5%
é 4202975
10.5%
č 443202
 
1.1%
Other values (6) 1384262
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39654239
99.4%
Space Separator 221601
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 4424576
11.2%
z 4202975
10.6%
p 4202975
10.6%
ů 4202975
10.6%
o 4202975
10.6%
b 4202975
10.6%
i 4202975
10.6%
l 4202975
10.6%
é 4202975
10.6%
č 443202
 
1.1%
Other values (5) 1162661
 
2.9%
Space Separator
ValueCountFrequency (%)
221601
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39654239
99.4%
Common 221601
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 4424576
11.2%
z 4202975
10.6%
p 4202975
10.6%
ů 4202975
10.6%
o 4202975
10.6%
b 4202975
10.6%
i 4202975
10.6%
l 4202975
10.6%
é 4202975
10.6%
č 443202
 
1.1%
Other values (5) 1162661
 
2.9%
Common
ValueCountFrequency (%)
221601
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30583486
76.7%
None 9292354
 
23.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 4424576
14.5%
z 4202975
13.7%
p 4202975
13.7%
o 4202975
13.7%
b 4202975
13.7%
i 4202975
13.7%
l 4202975
13.7%
e 248929
 
0.8%
n 248929
 
0.8%
t 221601
 
0.7%
None
ValueCountFrequency (%)
ů 4202975
45.2%
é 4202975
45.2%
č 443202
 
4.8%
á 221601
 
2.4%
ě 221601
 
2.4%

VyslEmise
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4203040
Missing (%)100.0%
Memory size64.1 MiB

DTKont
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8925559
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:24.108671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4033938
Coefficient of variation (CV)0.48517432
Kurtosis-1.1196307
Mean2.8925559
Median Absolute Deviation (MAD)1
Skewness0.14186066
Sum12157528
Variance1.9695142
MonotonicityNot monotonic
2023-04-01T14:53:24.162410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 920742
21.9%
3 916753
21.8%
2 851553
20.3%
4 832282
19.8%
5 638021
15.2%
6 41635
 
1.0%
7 2054
 
< 0.1%
ValueCountFrequency (%)
1 920742
21.9%
2 851553
20.3%
3 916753
21.8%
4 832282
19.8%
5 638021
15.2%
6 41635
 
1.0%
7 2054
 
< 0.1%
ValueCountFrequency (%)
7 2054
 
< 0.1%
6 41635
 
1.0%
5 638021
15.2%
4 832282
19.8%
3 916753
21.8%
2 851553
20.3%
1 920742
21.9%

ZavA
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7498727
Minimum0
Maximum38
Zeros2106231
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:24.234798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile7
Maximum38
Range38
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3868771
Coefficient of variation (CV)1.364029
Kurtosis2.6438641
Mean1.7498727
Median Absolute Deviation (MAD)0
Skewness1.5579824
Sum7354785
Variance5.6971823
MonotonicityNot monotonic
2023-04-01T14:53:24.310129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 2106231
50.1%
1 449953
 
10.7%
2 398423
 
9.5%
3 352838
 
8.4%
4 293666
 
7.0%
5 243116
 
5.8%
6 142213
 
3.4%
7 88843
 
2.1%
8 54600
 
1.3%
9 30489
 
0.7%
Other values (24) 42668
 
1.0%
ValueCountFrequency (%)
0 2106231
50.1%
1 449953
 
10.7%
2 398423
 
9.5%
3 352838
 
8.4%
4 293666
 
7.0%
5 243116
 
5.8%
6 142213
 
3.4%
7 88843
 
2.1%
8 54600
 
1.3%
9 30489
 
0.7%
ValueCountFrequency (%)
38 1
 
< 0.1%
35 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
29 1
 
< 0.1%
28 6
 
< 0.1%
27 3
 
< 0.1%
26 4
 
< 0.1%
25 12
< 0.1%
24 17
< 0.1%

ZavB
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14073099
Minimum0
Maximum42
Zeros3961301
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:24.392301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.73863031
Coefficient of variation (CV)5.2485262
Kurtosis91.265242
Mean0.14073099
Median Absolute Deviation (MAD)0
Skewness8.0121067
Sum591498
Variance0.54557474
MonotonicityNot monotonic
2023-04-01T14:53:24.460674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 3961301
94.2%
1 104092
 
2.5%
2 53017
 
1.3%
3 34147
 
0.8%
4 20224
 
0.5%
5 12263
 
0.3%
6 7143
 
0.2%
7 4279
 
0.1%
8 2444
 
0.1%
9 1540
 
< 0.1%
Other values (19) 2590
 
0.1%
ValueCountFrequency (%)
0 3961301
94.2%
1 104092
 
2.5%
2 53017
 
1.3%
3 34147
 
0.8%
4 20224
 
0.5%
5 12263
 
0.3%
6 7143
 
0.2%
7 4279
 
0.1%
8 2444
 
0.1%
9 1540
 
< 0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
32 1
 
< 0.1%
28 1
 
< 0.1%
26 4
 
< 0.1%
24 4
 
< 0.1%
23 5
 
< 0.1%
22 5
 
< 0.1%
21 9
< 0.1%
20 10
< 0.1%
19 17
< 0.1%

ZavC
Real number (ℝ)

SKEWED  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0081698009
Minimum0
Maximum15
Zeros4178080
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:24.526568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.12038571
Coefficient of variation (CV)14.735452
Kurtosis646.98823
Mean0.0081698009
Median Absolute Deviation (MAD)0
Skewness20.829745
Sum34338
Variance0.01449272
MonotonicityNot monotonic
2023-04-01T14:53:24.586154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 4178080
99.4%
1 18257
 
0.4%
2 4862
 
0.1%
3 1293
 
< 0.1%
4 380
 
< 0.1%
5 106
 
< 0.1%
6 38
 
< 0.1%
7 12
 
< 0.1%
9 5
 
< 0.1%
8 3
 
< 0.1%
Other values (3) 4
 
< 0.1%
ValueCountFrequency (%)
0 4178080
99.4%
1 18257
 
0.4%
2 4862
 
0.1%
3 1293
 
< 0.1%
4 380
 
< 0.1%
5 106
 
< 0.1%
6 38
 
< 0.1%
7 12
 
< 0.1%
8 3
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
12 1
 
< 0.1%
10 2
 
< 0.1%
9 5
 
< 0.1%
8 3
 
< 0.1%
7 12
 
< 0.1%
6 38
 
< 0.1%
5 106
 
< 0.1%
4 380
 
< 0.1%
3 1293
< 0.1%

Zav0
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062830951
Minimum0
Maximum26
Zeros3955321
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:24.649549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25899103
Coefficient of variation (CV)4.1220295
Kurtosis44.97822
Mean0.062830951
Median Absolute Deviation (MAD)0
Skewness4.5657418
Sum264081
Variance0.067076356
MonotonicityNot monotonic
2023-04-01T14:53:24.709929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3955321
94.1%
1 231854
 
5.5%
2 15436
 
0.4%
3 393
 
< 0.1%
4 32
 
< 0.1%
5 2
 
< 0.1%
12 1
 
< 0.1%
26 1
 
< 0.1%
ValueCountFrequency (%)
0 3955321
94.1%
1 231854
 
5.5%
2 15436
 
0.4%
3 393
 
< 0.1%
4 32
 
< 0.1%
5 2
 
< 0.1%
12 1
 
< 0.1%
26 1
 
< 0.1%
ValueCountFrequency (%)
26 1
 
< 0.1%
12 1
 
< 0.1%
5 2
 
< 0.1%
4 32
 
< 0.1%
3 393
 
< 0.1%
2 15436
 
0.4%
1 231854
 
5.5%
0 3955321
94.1%

Zav1
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41430465
Minimum0
Maximum14
Zeros3059185
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:24.771482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79553947
Coefficient of variation (CV)1.9201799
Kurtosis6.6653572
Mean0.41430465
Median Absolute Deviation (MAD)0
Skewness2.3157403
Sum1741339
Variance0.63288305
MonotonicityNot monotonic
2023-04-01T14:53:24.830998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 3059185
72.8%
1 709930
 
16.9%
2 312338
 
7.4%
3 91057
 
2.2%
4 22759
 
0.5%
5 5353
 
0.1%
6 1603
 
< 0.1%
7 538
 
< 0.1%
8 171
 
< 0.1%
9 70
 
< 0.1%
Other values (4) 36
 
< 0.1%
ValueCountFrequency (%)
0 3059185
72.8%
1 709930
 
16.9%
2 312338
 
7.4%
3 91057
 
2.2%
4 22759
 
0.5%
5 5353
 
0.1%
6 1603
 
< 0.1%
7 538
 
< 0.1%
8 171
 
< 0.1%
9 70
 
< 0.1%
ValueCountFrequency (%)
14 2
 
< 0.1%
12 3
 
< 0.1%
11 5
 
< 0.1%
10 26
 
< 0.1%
9 70
 
< 0.1%
8 171
 
< 0.1%
7 538
 
< 0.1%
6 1603
 
< 0.1%
5 5353
 
0.1%
4 22759
0.5%

Zav2
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084335862
Minimum0
Maximum6
Zeros3878715
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:24.898376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30462104
Coefficient of variation (CV)3.611999
Kurtosis17.673445
Mean0.084335862
Median Absolute Deviation (MAD)0
Skewness3.9283181
Sum354467
Variance0.092793981
MonotonicityNot monotonic
2023-04-01T14:53:24.952063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3878715
92.3%
1 296518
 
7.1%
2 25693
 
0.6%
3 1914
 
< 0.1%
4 182
 
< 0.1%
5 15
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
0 3878715
92.3%
1 296518
 
7.1%
2 25693
 
0.6%
3 1914
 
< 0.1%
4 182
 
< 0.1%
5 15
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
5 15
 
< 0.1%
4 182
 
< 0.1%
3 1914
 
< 0.1%
2 25693
 
0.6%
1 296518
 
7.1%
0 3878715
92.3%

Zav3
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.085941842
Minimum0
Maximum6
Zeros3887919
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:25.013145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32238379
Coefficient of variation (CV)3.7511855
Kurtosis23.508201
Mean0.085941842
Median Absolute Deviation (MAD)0
Skewness4.3711947
Sum361217
Variance0.10393131
MonotonicityNot monotonic
2023-04-01T14:53:25.067657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3887919
92.5%
1 275339
 
6.6%
2 34263
 
0.8%
3 4834
 
0.1%
4 587
 
< 0.1%
5 86
 
< 0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
0 3887919
92.5%
1 275339
 
6.6%
2 34263
 
0.8%
3 4834
 
0.1%
4 587
 
< 0.1%
5 86
 
< 0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
6 12
 
< 0.1%
5 86
 
< 0.1%
4 587
 
< 0.1%
3 4834
 
0.1%
2 34263
 
0.8%
1 275339
 
6.6%
0 3887919
92.5%

Zav4
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30132856
Minimum0
Maximum21
Zeros3283109
Zeros (%)78.1%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:25.134840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.66054966
Coefficient of variation (CV)2.1921243
Kurtosis12.758654
Mean0.30132856
Median Absolute Deviation (MAD)0
Skewness2.9060421
Sum1266496
Variance0.43632585
MonotonicityNot monotonic
2023-04-01T14:53:25.196053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 3283109
78.1%
1 660289
 
15.7%
2 198194
 
4.7%
3 45117
 
1.1%
4 10790
 
0.3%
5 3447
 
0.1%
6 1243
 
< 0.1%
7 486
 
< 0.1%
8 212
 
< 0.1%
9 88
 
< 0.1%
Other values (8) 65
 
< 0.1%
ValueCountFrequency (%)
0 3283109
78.1%
1 660289
 
15.7%
2 198194
 
4.7%
3 45117
 
1.1%
4 10790
 
0.3%
5 3447
 
0.1%
6 1243
 
< 0.1%
7 486
 
< 0.1%
8 212
 
< 0.1%
9 88
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
16 1
 
< 0.1%
15 4
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
 
< 0.1%
11 17
 
< 0.1%
10 34
 
< 0.1%
9 88
< 0.1%
8 212
< 0.1%

Zav5
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17863332
Minimum0
Maximum10
Zeros3574221
Zeros (%)85.0%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:25.259450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.46328801
Coefficient of variation (CV)2.5935139
Kurtosis11.534574
Mean0.17863332
Median Absolute Deviation (MAD)0
Skewness3.0121175
Sum750803
Variance0.21463578
MonotonicityNot monotonic
2023-04-01T14:53:25.322807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 3574221
85.0%
1 523208
 
12.4%
2 92551
 
2.2%
3 10533
 
0.3%
4 1923
 
< 0.1%
5 474
 
< 0.1%
6 98
 
< 0.1%
7 19
 
< 0.1%
8 8
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 3574221
85.0%
1 523208
 
12.4%
2 92551
 
2.2%
3 10533
 
0.3%
4 1923
 
< 0.1%
5 474
 
< 0.1%
6 98
 
< 0.1%
7 19
 
< 0.1%
8 8
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 3
 
< 0.1%
8 8
 
< 0.1%
7 19
 
< 0.1%
6 98
 
< 0.1%
5 474
 
< 0.1%
4 1923
 
< 0.1%
3 10533
 
0.3%
2 92551
 
2.2%
1 523208
12.4%

Zav6
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75074303
Minimum0
Maximum19
Zeros2440814
Zeros (%)58.1%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:25.387642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0937296
Coefficient of variation (CV)1.4568628
Kurtosis3.6114039
Mean0.75074303
Median Absolute Deviation (MAD)0
Skewness1.6984354
Sum3155403
Variance1.1962445
MonotonicityNot monotonic
2023-04-01T14:53:25.452306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2440814
58.1%
1 859421
 
20.4%
2 569145
 
13.5%
3 228325
 
5.4%
4 71813
 
1.7%
5 22291
 
0.5%
6 7148
 
0.2%
7 2615
 
0.1%
8 864
 
< 0.1%
9 364
 
< 0.1%
Other values (9) 240
 
< 0.1%
ValueCountFrequency (%)
0 2440814
58.1%
1 859421
 
20.4%
2 569145
 
13.5%
3 228325
 
5.4%
4 71813
 
1.7%
5 22291
 
0.5%
6 7148
 
0.2%
7 2615
 
0.1%
8 864
 
< 0.1%
9 364
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 3
 
< 0.1%
14 8
 
< 0.1%
13 10
 
< 0.1%
12 33
 
< 0.1%
11 51
 
< 0.1%
10 130
 
< 0.1%
9 364
< 0.1%

Zav7
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0070860615
Minimum0
Maximum6
Zeros4175613
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:25.520500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.091233166
Coefficient of variation (CV)12.875018
Kurtosis286.00802
Mean0.0070860615
Median Absolute Deviation (MAD)0
Skewness14.996985
Sum29783
Variance0.0083234907
MonotonicityNot monotonic
2023-04-01T14:53:25.574752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4175613
99.3%
1 25351
 
0.6%
2 1856
 
< 0.1%
3 168
 
< 0.1%
4 46
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 4175613
99.3%
1 25351
 
0.6%
2 1856
 
< 0.1%
3 168
 
< 0.1%
4 46
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 4
 
< 0.1%
4 46
 
< 0.1%
3 168
 
< 0.1%
2 1856
 
< 0.1%
1 25351
 
0.6%
0 4175613
99.3%

Zav8
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012870446
Minimum0
Maximum8
Zeros4165235
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:25.638593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15018895
Coefficient of variation (CV)11.669288
Kurtosis256.85445
Mean0.012870446
Median Absolute Deviation (MAD)0
Skewness14.535188
Sum54095
Variance0.02255672
MonotonicityNot monotonic
2023-04-01T14:53:25.698573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 4165235
99.1%
1 25134
 
0.6%
2 9705
 
0.2%
3 2427
 
0.1%
4 447
 
< 0.1%
5 75
 
< 0.1%
6 13
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 4165235
99.1%
1 25134
 
0.6%
2 9705
 
0.2%
3 2427
 
0.1%
4 447
 
< 0.1%
5 75
 
< 0.1%
6 13
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 3
 
< 0.1%
6 13
 
< 0.1%
5 75
 
< 0.1%
4 447
 
< 0.1%
3 2427
 
0.1%
2 9705
 
0.2%
1 25134
 
0.6%
0 4165235
99.1%

Zav9
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00074517492
Minimum0
Maximum5
Zeros4200626
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size64.1 MiB
2023-04-01T14:53:25.762704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.034170851
Coefficient of variation (CV)45.856147
Kurtosis4086.2724
Mean0.00074517492
Median Absolute Deviation (MAD)0
Skewness57.537757
Sum3132
Variance0.0011676471
MonotonicityNot monotonic
2023-04-01T14:53:25.822239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 4200626
99.9%
1 1841
 
< 0.1%
2 452
 
< 0.1%
3 99
 
< 0.1%
4 20
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 4200626
99.9%
1 1841
 
< 0.1%
2 452
 
< 0.1%
3 99
 
< 0.1%
4 20
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 20
 
< 0.1%
3 99
 
< 0.1%
2 452
 
< 0.1%
1 1841
 
< 0.1%
0 4200626
99.9%

StariDnu
Real number (ℝ)

Distinct23850
Distinct (%)0.6%
Missing28153
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean5552.5632
Minimum-2853
Maximum98656
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)< 0.1%
Memory size64.1 MiB
2023-04-01T14:53:25.900634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2853
5-th percentile1723
Q13281
median5369
Q37154
95-th percentile10346
Maximum98656
Range101509
Interquartile range (IQR)3873

Descriptive statistics

Standard deviation3123.2212
Coefficient of variation (CV)0.56248279
Kurtosis7.7053018
Mean5552.5632
Median Absolute Deviation (MAD)1916
Skewness1.7004361
Sum2.3181324 × 1010
Variance9754510.9
MonotonicityNot monotonic
2023-04-01T14:53:25.990229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8776 4761
 
0.1%
9141 4571
 
0.1%
8411 4405
 
0.1%
9506 4327
 
0.1%
9872 3372
 
0.1%
8045 3102
 
0.1%
10237 2682
 
0.1%
12063 2649
 
0.1%
1934 2276
 
0.1%
1927 2273
 
0.1%
Other values (23840) 4140469
98.5%
(Missing) 28153
 
0.7%
ValueCountFrequency (%)
-2853 1
 
< 0.1%
-2771 1
 
< 0.1%
-2644 1
 
< 0.1%
-2599 1
 
< 0.1%
-1760 1
 
< 0.1%
403 1
 
< 0.1%
407 3
 
< 0.1%
408 10
< 0.1%
411 5
< 0.1%
412 5
< 0.1%
ValueCountFrequency (%)
98656 1
 
< 0.1%
98573 1
 
< 0.1%
98531 1
 
< 0.1%
98408 1
 
< 0.1%
50910 1
 
< 0.1%
45191 1
 
< 0.1%
45015 52
 
< 0.1%
44935 177
< 0.1%
44929 1
 
< 0.1%
44928 1
 
< 0.1%

Interactions

2023-04-01T14:52:52.911991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:47.323251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:51.701319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:56.016376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:00.522751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:04.730934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:08.975974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:13.204814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:17.730247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:22.076057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:26.395158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:30.827464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:35.208929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:39.822935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:44.193379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:48.569237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:53.254146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:47.596595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:51.960718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:56.298143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:00.782521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:04.990406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:09.240089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:13.477502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:17.997377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:22.337817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:26.667115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:31.100087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:35.493393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:40.091665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:44.461159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:48.828138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:53.590972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:47.866251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:52.231309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:56.583683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:01.043050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:05.249984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:09.501578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:13.757936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:18.264571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:22.602716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:26.940224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:31.374851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:35.781275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:40.359586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:44.741162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:49.093331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:53.941277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:48.146883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:52.502074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:56.864072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:01.301416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:05.515000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:09.765655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:14.040943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:18.548565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:22.870670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:27.213669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:31.652772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:36.073451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:40.637546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:45.021291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:49.361556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:54.280063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:48.410045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:52.764508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:57.134726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:01.553025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:05.766188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:10.016458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:14.309686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:18.812067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:23.130024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:27.481192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:31.913956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:36.351567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:40.897636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:45.280890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:49.623429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:54.630430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:48.684052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:53.035620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:57.417143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:01.817263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:06.032401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:10.278244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:14.590513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:19.081405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:23.404114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:27.758837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:32.188159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:36.640954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:41.172143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:45.550075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:49.892354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:54.963791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:48.950777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:53.300251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:57.694572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:02.076528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:06.292175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:10.535498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:14.863773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:19.349481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:23.671533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:28.028877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:32.451554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:36.922611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:41.434613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:45.814889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:50.164281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:55.314790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:49.222414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:53.572514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:57.976875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:02.341770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:06.558091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:10.799149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:15.146329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:19.616464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:23.940551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:28.302420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:32.724046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:37.218443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:41.709688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:46.091557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:50.442709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:55.669890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:49.490772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:53.839365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:58.256486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:02.604271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:06.821062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:11.060600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:15.423985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:19.882787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:24.207281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:28.575434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:32.991329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:37.503757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:41.980680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:46.356579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:50.707374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:56.019916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:49.762480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:54.107117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:58.533590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:02.864770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:07.083091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:11.321877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:15.719684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:20.153770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:24.471946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:28.847448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:33.262105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:37.794147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:42.251529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:46.634191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:50.976754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:56.354780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:50.034394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:54.364409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:58.807904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:03.120424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:07.337949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:11.577442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:16.001164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:20.418705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:24.734220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:29.125199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:33.525788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:38.077047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:42.516889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:46.908971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:51.231713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:56.698419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:50.309520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:54.628334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:59.083786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:03.376751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:07.598659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:11.834600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:16.281788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:20.688953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:24.999931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:29.398476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:33.792179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:38.358167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:42.792331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:47.174363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:51.491546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:57.043983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:50.581636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:54.894003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:59.365352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:03.641310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:07.860129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:12.099254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:16.561710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:20.961351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:25.270344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:29.676180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:34.069786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:38.644591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:43.068633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:47.440281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:51.756540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:57.405329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:50.846000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:55.157918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:59.644474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:03.899234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:08.123152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:12.356270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:16.842829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:21.226644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:25.535556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:29.948153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:34.343098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:38.929357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:43.341216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:47.723695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:52.017531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:57.753268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:51.117093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:55.423588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:59.926202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:04.164535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:08.385329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:12.618956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:17.123053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:21.494696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:25.801615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:30.234558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:34.617873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:39.218607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:43.614236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:47.994961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:52.280901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:58.064577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:51.432607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:51:55.736976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:00.256356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:04.471739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:08.699220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:12.932384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:17.456143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:21.806062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:26.116522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:30.554827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:34.924951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:39.549029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:43.925487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:48.304431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T14:52:52.584034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-01T14:53:00.456356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-01T14:53:05.932708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-01T14:53:17.364695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKVyslEmiseDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu
STK
3131pravidelná1/12-18022021-01-21-ACKNÁKLADNÍ PŘÍVĚS2500O21998-07-020způsobiléNaN410000000010008959.0
3522pravidelnáTM1V023106S0000232021-01-26-ACKNÁKLADNÍ PŘÍVĚS2000 AO22007-04-120způsobiléNaN200000000000005753.0
3523Evidenční kontrolaWAFZLAF103K0267132021-01-26-ACKERMANNNÁKLADNÍ PŘÍVĚSz-la-f 10,5/7,4eO32004-12-100způsobiléNaN200000000000006606.0
3225pravidelnáWAFZKAF18DK0366692021-01-13NaNACKERMANNPŘÍPOJNÉ VOZIDLOZ-KA-F18/7O42014-01-210způsobiléNaN330002000010003277.0
3622pravidelnáWAFZPAF11EK0370602021-01-22NaNACKERMANNNÁKLADNÍ PŘÍVĚSZ-PA-F10.5O42014-05-120způsobiléNaN550001001030003166.0
3107Před registracíWAFZKAF11BK0350892021-01-18NaNACKERMANNPŘÍPOJNÉ VOZIDLOZ-KA-F10O42011-09-270způsobiléNaN110000000010004124.0
3622pravidelnáWAFXXXXXXYK0243152021-01-14NaNACKERMANNNÁKLADNÍ PŘÍVĚS18/7.3 ELO42001-06-260způsobiléNaN470013000210007869.0
3243pravidelnáWAFLAF11X5K0291582021-01-05-ACKERMANNNÁKLADNÍ PŘÍVĚSLA-FO42005-10-060způsobiléNaN240001001020006306.0
3618pravidelnáWAFXXXXXXXK0237122021-01-06NaNACKERMANNNÁKLADNÍ PŘÍVĚSI-AEF19-7.4/126O42001-03-120způsobiléNaN360012000030007975.0
3323pravidelnáWAFXXXXXXYK0241292021-01-05NaNACKERMANNNÁKLADNÍ PŘÍVĚSI-PA-F 18/7.1ELO42002-07-020způsobiléNaN250002001110007498.0
DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKVyslEmiseDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu
STK
6732Před registrací000F5G4L41PT023552021-12-311605ZETORTRAKTOR KOLOVÝFORTERA 135T1a2013-12-203912způsobiléNaN500000000000003389.0
3125pravidelnáWP0ZZZ99ZBS7608162021-12-31MA170SPORSCHEOSOBNÍ AUTOMOBIL911M12010-09-3065076způsobiléNaN500000000000004566.0
7718Technická silniční kontrolaVH1C39C14G1B008992021-12-30NaNBENALUNÁKLADNÍ NÁVĚSOPTILINERO4NaT0způsobiléNaN40000000000000NaN
7718Technická silniční kontrolaTNT8P6N23KK0026992021-12-30NaNTATRATRAKTOR KOLOVÝT 158 /3T1bNaT81105způsobiléNaN40000000000000NaN
7700Technická silniční kontrolaWV1ZZZ2EZ860416702021-12-28NaNVOLKSWAGENNÁKLADNÍ AUTOMOBILCRAFTERN1NaT286954částečně způsobiléNaN20300001010010NaN
7700Technická silniční kontrolaWBAKV210500R293442021-12-28NaNBMWOSOBNÍ AUTOMOBILX6 XDrive30d (G6X)M1NaT117600částečně způsobiléNaN20100001000000NaN
7700Technická silniční kontrolaWBA7U81060CG991782021-12-30NaNBMWOSOBNÍ AUTOMOBIL740 D XDRIVE(7L)M1NaT15925částečně způsobiléNaN40100001000000NaN
7700Technická silniční kontrolaZFA312000008874802021-12-30NaNFIATOSOBNÍ AUTOMOBILABARTH 500 1.4 T 135M1NaT195191částečně způsobiléNaN40200002000000NaN
7706Technická silniční kontrolaYS2S4Y200055253472021-12-02NaNSCANIANÁKLADNÍ AUTOMOBILS540N3NaT438343způsobiléNaN40000000000000NaN
7707Technická silniční kontrolaVF611A367KD0208412021-12-16NaNRENAULTNÁKLADNÍ AUTOMOBILHD001N3NaT205216částečně způsobiléNaN40200000002000NaN

Duplicate rows

Most frequently occurring

DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu# duplicates
1676Před registracíTMBJD46Y3440948242021-06-10BBZŠKODAOSOBNÍ AUTOMOBILFABIA (6Y)M12004-05-21169879způsobilé430002000010006809.04
4993pravidelnáUU10SDA35536877512021-11-05K7M A8DACIAOSOBNÍ AUTOMOBILDOKKERM12015-10-1561190způsobilé500000000000002645.04
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835Evidenční kontrolaWAUZZZ8LZYA1041902021-11-22AHFAUDIOSOBNÍ AUTOMOBILA3M12000-05-12245158způsobilé100000000000008279.03
866Evidenční kontrolaWBA5N61000D0880592021-12-10N57D30ABMWOSOBNÍ AUTOMOBIL530 DM12013-11-06248087způsobilé500000000000003433.03